Rust_RL VS open_spiel

Nothing wrong with that! I also learned Rust while writing DL/RL algorithms: https://github.com/ZuseZ4/Rust_RL/

Nice, also worked on tic-tac-toe / Neural Networks / Agents to get into Rust / RL: https://github.com/ZuseZ4/Rust_RL/

I'm just finishing undergrad studies and wrote a Reinforcement Learning Library (https://github.com/ZuseZ4/Rust_RL) including double-deep-q learning with replay buffer in pure rust to learn the language. I'm also trying to get Rust into shape for data science, but I'd vote against using it as an example in introduction classes. Pure Python (which wraps around C++) will usually be simpler and faster for users, leaving few reasons to focus on this in a lecture. Furthermore, even if we are into ML, there are probably quite a few people who'd pick such a lesson to learn about Rust, not about Machine Learning. For the latter, there are at least in the Universities I know, more than enough offers.

Deep mind actually has a repository where they applied this algorithm for incomplete-knowledge games. You could use it for reference: https://github.com/deepmind/open_spiel/tree/master/open_spiel/python/algorithms

+1. You can also find an implementation of Clobber and AlphaZero (and many other basic RL algorithms) in OpenSpiel: https://github.com/deepmind/open_spiel

You can find a lot of code online if you look, and many of these applied to Poker. There's a general implementation of both in Python and C++ in OpenSpiel, with some examples applied to small poker games. It's nice code to learn from because the algorithms operate over generic game descriptions, so there aren't game-specific design choices mixed up with the implementation of the algorithms, and you can create your own poker game and just run them on it.

And many other additions and improvements. See all the details here: https://github.com/deepmind/open_spiel/releases/tag/v1.3

I would checkout the openspiel package. It's main focus is RL in games (multi-agent environments). You'll find RL examples there and games that are small enough to solve without deep RL. There's also a wide range of environments from fully cooperative to adversarial zero-sum.

Hi, you can check out OpenSpiel: https://github.com/deepmind/open_spiel/

as for algorithms , openspiel repository has few implementations some of these are not related to imperfect information games , and others are not for multiagent environment and others are tabular algorithms .

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

I've had some success using Action Masking - you can refer to here https://github.com/deepmind/open_spiel/blob/120420a74a69354d64c10b51cd129d4587f9f325/open_spiel/python/algorithms/dqn.py but for DQN you need to mask out q values for invalid actions (as well as masking them during prediction). In my case I'm able to place my mask in the observation so can fetch it quite easily during prediction but if that's not possible you could query it from the environment and store it in the replay buffer (like they do in the link I shared)

Take a look at this simple implementation: https://github.com/deepmind/open_spiel/blob/master/open_spiel/algorithms/minimax.cc